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filipposML t1_j9vc6dw wrote

Reply to comment by mosquitoLad in [D] What is the correct term for a non-GAN system where two or more networks compete as part of training? by mosquitoLad

Indeed, the generative model produces data points, and the discriminative one classifies them together with the real data. I think that for your purposes it is easier to refer to your algorithm as "adversarial in nature". You are using games where the algorithms are expected to reach a Nash equilibrium, but also there is no gradient (presumably) from one agent to another.

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mosquitoLad OP t1_j9vq86y wrote

Nash Equilibrium is a new term for me; and you are right, that seems like a logical end state. I do not understand what a gradient is in this context; would this terminology apply when information is being processed by a series of agents, each having a direct influence on the quality of the output of other agents?

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[deleted] t1_j9vqstv wrote

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mosquitoLad OP t1_j9vrjsb wrote

Completely fair

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[deleted] t1_j9vse6h wrote

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mosquitoLad OP t1_j9vtp3b wrote

Thanks. It's not so big as a seminar. I'm in a public speaking course where each primary speech falls into a certain criteria, this one being Educational. I'm a senior CS major, the majority are freshman non-CS, so I've to make sure whatever I say is both accurate and explained in simpler terms (less 3Blue1Brown, more Code Bullet I guess).

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clueless1245 t1_j9wzcbn wrote

Idk what he means specifically by the "gradient being passed between" two agents but in a GAN (part of) the loss function of the generator is the inverse of (part of) the loss function of the discriminator, so the gradients calculated at generator output and discriminator output are linked.

A less formal way of saying it: The generator's gradient depends on the discriminator's loss.

This should be true for any adversarial game, I would think?

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mosquitoLad OP t1_j9x2guv wrote

The less formal way conveys the concept better; and it makes sense, the worse the discriminator performs (whether it is overly sensitive or less sensitive when attempting to sus out the validity of assets), the worse the generator performs, at least with regard to the quality of the output for human purposes. If I'm understanding the use of gradient correctly, the generator become trapped in a local minimum because it discovers how to consistently exploit the weaknesses of the discriminator.

I don't know for sure if it always applies; you could apply an evolutionary algorithm where two or more competing populations are tackling the same problem from opposing sides, and have relatively infrequent breeding between members of the populations, motivating avoidance of bottlenecking while enabling the development of unique solutions; over several generations, any short term loss should serve to be a long term gain. But, I guess they'd still be dependent on how the scoring system works (equivalent to loss function?).

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clueless1245 t1_j9x3dlc wrote

Its also an issue for generator training though if the discriminator gets 100% all the time, if I remember correctly. Theres various stuff you can look up to make training more stable which I don't have on hand rn.

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